2023/05/31 by Haopeng Zhang, Xiao Liu, Zhang, Haopeng +3 · 1 citation
Computer Science · Biochemistry, Genetics and Molecular Biology · #Topic Modeling #Advanced Text Analysis Techniques #Biomedical Text Mining and Ontologies
paper · pdf · doi:10.48550/arxiv.2306.00177
The extended structural context has made scientific paper summarization a challenging task. This paper proposes CHANGES, a contrastive hierarchical graph neural network for extractive scientific paper summarization. CHANGES represents a scientific paper with a hierarchical discourse graph and learns effective sentence representations with dedicated designed hierarchical graph information aggregation. We also propose a graph contrastive learning module to learn global theme-aware sentence representations. Extensive experiments on the PubMed and arXiv benchmark datasets prove the effectiveness of CHANGES and the importance of capturing hierarchical structure information in modeling scientific papers.